library(tidyverse)
library(here)
library(sf)
library(infer)
library(ggridges)
library(modelr)
library(skimr)
library(ggfortify)
source(here("scripts/cleaning_script.R"))
regional_domestic_tourism_individual <- read_csv(here("data/clean_data/regional_domestic_tourism_individual_clean.csv"))
regional_domestic_tourism_non_gb_clean <- read_csv(here("data/clean_data/regional_domestic_tourism_non_gb_clean.csv"))
international_visits <- read_csv(here("data/clean_data/international_visits_clean.csv"))
tourism_businesses <- read_csv(here("data/clean_data/tourism_businesses_clean.csv"))
dom_int_summary <- read_csv(here("data/clean_data/dom_int_summary_clean.csv"))
local_authority_geo <- st_read(dsn = "data/geo_data/", layer = "pub_las")
colour_scheme <- c("#540453", "#1d1d65", "#970061", "#b6cb2b", "#9fd6f3")
dom_int_summary %>%
filter(dom_int == "Domestic") %>%
skim()
── Data Summary ────────────────────────
Values
Name Piped data
Number of rows 11
Number of columns 4
_______________________
Column type frequency:
character 1
numeric 3
________________________
Group variables None
dom_int_summary %>%
filter(dom_int == "International") %>%
skim()
── Data Summary ────────────────────────
Values
Name Piped data
Number of rows 11
Number of columns 4
_______________________
Column type frequency:
character 1
numeric 3
________________________
Group variables None
dom_int_summary %>%
filter(dom_int == "Domestic") %>%
summarise(visits_iqr= IQR(visits),
expenditure_iqr= IQR(spend))
dom_int_summary %>%
filter(dom_int == "International") %>%
summarise(visits_iqr= IQR(visits),
expenditure_iqr= IQR(spend))
dom_int_summary %>%
filter(dom_int == "Domestic") %>%
summarise(median_visits = median(visits),
median_spend = median(spend))
NA
dom_int_summary %>%
filter(dom_int == "International") %>%
summarise(median_visits = median(visits),
median_spend = median(spend))
dom_int_summary %>%
ggplot() +
geom_line(aes(x = year, y = visits, colour = dom_int),
size = 2) +
geom_point(aes(x = year, y = visits, colour = dom_int),
size = 4) +
ylim(0, 15000) +
labs(x = "\n Year",
y = "Visitors (Thousands) \n",
title = "Annual Visits",
subtitle = "Domestic & International Overnight Vistors",
colour = "") +
scale_x_continuous(breaks = c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019)) +
scale_colour_manual(values = c("Domestic" = "#540453",
"International" = "#9fd6f3")) +
theme(plot.title = element_text(size = 15, face = "bold"),
plot.subtitle = element_text(size = 10),
panel.background = element_rect(fill = "white"),
panel.grid = element_line(colour = "grey90",
linetype = "dashed"))
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.
dom_int_summary %>%
ggplot() +
geom_line(aes(x = year, y = spend, colour = dom_int),
size = 2) +
geom_point(aes(x = year, y = spend, colour = dom_int),
size = 4) +
ylim(0, 4000) +
labs(x = "\n Year",
y = "Expenditure (Million GBP) \n",
title = "Annual Expenditure",
subtitle = "Domestic & International Overnight Vistors",
colour = "") +
scale_x_continuous(breaks = c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019)) +
scale_colour_manual(values = c("Domestic" = "#540453",
"International" = "#9fd6f3")) +
theme(plot.title = element_text(size = 15, face = "bold"),
plot.subtitle = element_text(size = 10),
panel.background = element_rect(fill = "white"),
panel.grid = element_line(colour = "grey90",
linetype = "dashed"))
regional_data_joined %>%
group_by(local_authority) %>%
summarise(mean_visits = mean(visits)) %>%
arrange(desc(mean_visits))
Simple feature collection with 32 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 5512.998 ymin: 530250.8 xmax: 470332 ymax: 1220302
Projected CRS: OSGB36 / British National Grid